Information Extraction (IE) is the task of turning the unstructured information expressed in natural language (NL) text into a structured representation in the form of relational tuples consisting of a set of arguments and a phrase denoting a semantic relation between them: \(\langle \)arg1; rel; arg2\(\rangle \) . Unlike traditional IE methods, Open IE is not limited to a small set of target relations known in advance, but rather extracts all types of relations found in a text. In that way, it facilitates the domain-independent discovery of relations extracted from text and scales to large, heterogeneous corpora such as the Web. Since its introduction by Banko07, a large body of work on the task of Open IE has been described. By analyzing the output of state-of-the-art systems (e.g., [1]}, [2]}, [3]}), we observed three common shortcomings.
